CLASSIFYING BENIGN AND MALIGNANT MASSES USING STATISTICAL MEASURES

Breast cancer is the primary and most common disease found in women which causes second highest rate of death after lung cancer. The digital mammogram is the X-ray of breast captured for the analysis, interpretation and diagnosis. According to Breast Imaging Reporting and Data System (BIRADS) benign and malignant can be differentiated using its shape, size and density, which is how radiologist visualize the mammograms. According to BIRADS mass shape characteristics, benign masses tend to have round, oval, lobular in shape and malignant masses are lobular or irregular in shape. Measuring regular and irregular shapes mathematically is found to be a difficult task, since there is no single measure to differentiate various shapes. In this paper, the malignant and benign masses present in mammogram are classified using Hue, Saturation and Value (HSV) weight function based statistical measures. The weight function is robust against noise and captures the degree of gray content of the pixel. The statistical measures use gray weight value instead of gray pixel value to effectively discriminate masses. The 233 mammograms from the Digital Database for Screening Mammography (DDSM) benchmark dataset have been used. The PASW data mining modeler has been used for constructing Neural Network for identifying importance of statistical measures. Based on the obtained important statistical measure, the C5.0 tree has been constructed with 60-40 data split. The experimental results are found to be encouraging. Also, the results will agree to the standard specified by the American College of Radiology-BIRADS Systems.

[1]  Christopher J. Taylor,et al.  Web Services for the DDSM and Digital Mammography Research , 2006, Digital Mammography / IWDM.

[2]  A. Mencattini,et al.  Features Extraction to Classify Microcalcification Clusters in Mammography , 2008 .

[3]  Shamik Sural,et al.  Robust histogram generation from the HSV space based on visual colour perception , 2008 .

[4]  L. Liberman,et al.  Breast imaging reporting and data system (BI-RADS). , 2002, Radiologic clinics of North America.

[5]  N.H. Younan,et al.  Applying modular classifiers to mammographic mass classification , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Jesus A. Gonzalez,et al.  Data Mining with Decision Trees and Neural Networks for Calcification Detection in Mammograms , 2004, MICAI.

[7]  A. Retico,et al.  An automatic system to discriminate malignant from benign massive lesions on mammograms , 2006 .

[8]  Joseph Y. Lo,et al.  Cluster analysis of BI-RADS descriptions of biopsy-proven breast lesions , 2002, SPIE Medical Imaging.

[9]  B. Tranel.  The sounds of French: L and R , 1987 .

[10]  J. Wolfe.  Breast patterns as an index of risk for developing breast cancer. , 1976, AJR. American journal of roentgenology.

[11]  Heng-Da Cheng,et al.  Computer-aided detection and classification of microcalcifications in mammograms: a survey , 2003, Pattern Recognit..

[12]  R. Egan,et al.  Breast cancer mammography patterns , 1977, Cancer.

[13]  R L Egan,et al.  Breast cancer mammography patterns. , 1977, Cancer.

[14]  Sameer Singh,et al.  Detection of masses in mammograms using texture features , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[15]  Kunio Doi.  Computer-aided diagnosis: potential usefulness in diagnostic radiology and telemedicine , 1995, Proceedings of the National Forum: Military Telemedicine On-Line Today Research, Practice, and Opportunities.

[16]  P. Deepa Shenoy,et al.  Classification of Mammograms Using Decision Trees , 2006, 2006 10th International Database Engineering and Applications Symposium (IDEAS'06).

[17]  A. Retico,et al.  Mammogram Segmentation by Contour Searching and Mass Lesions Classification With Neural Network , 2004, IEEE Transactions on Nuclear Science.

[18]  Shamik Sural,et al.  An Integrated Color and Intensity Co-occurrence Matrix , 2007, Pattern Recognit. Lett..

[19]  H. Hegedus,et al.  Exploring New Frontiers of Instrumentation and Methods for Electrical and Electronic Measurements , 2008 .

[20]  Alan C. Bovik,et al.  Classification of mammographic lesions into BI-RADS shape categories using the beamlet transform , 2005, SPIE Medical Imaging.

[21]  A. Vadivel,et al.  An Automated Classification of Mammogram Masses using Statistical Measures , 2009, IICAI.

[22]  D. Vanel.  The American College of Radiology (ACR) Breast Imaging and Reporting Data System (BI-RADS): a step towards a universal radiological language? , 2007, European journal of radiology.

[23]  Edward J. Delp,et al.  Normal mammogram classification based on regional analysis , 2002, The 2002 45th Midwest Symposium on Circuits and Systems, 2002. MWSCAS-2002..

[24]  A. Kandaswamy,et al.  Breast Tissue Classification Using Statistical Feature Extraction Of Mammograms , 2006 .

[25]  P. Pani,et al.  GEMS: Underwater spectrometer for long-term radioactivity measurements , 2011 .

[26]  Osmar R. Zaïane,et al.  Mammography Classification By an Association Rule-based Classifier , 2002, MDM/KDD.

[27]  Kuldeep Kumar,et al.  A Hybrid Classifier for Mass Classification with Different Kinds of Features in Mammography , 2005, FSKD.

[28]  A. Vadivel,et al.  A Soft-Decision Approach for Microcalcification Mass Identification from Digital Mammogram , 2008 .

[29]  Rangaraj M. Rangayyan,et al.  A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs , 2007, J. Frankl. Inst..

[30]  Asoke K. Nandi,et al.  Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection , 2008, Comput. Medical Imaging Graph..

[31]  A. Kandaswamy,et al.  Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms , 2007, Comput. Medical Imaging Graph..

[32]  G. Ertas,et al.  Feature extraction from mammographic mass shapes and development of a mammogram database , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.